GOA Flathead sole sensitivity runs 2022

Author

Maia Sosa Kapur maia.kapur@noaa.gov

This document contains the sensitivity runs mentioned in the “responses to SSC…” and “data gaps and future research” sections of the 2022 SAFE. Because they are not presented as alternative models, we elected to provide these as online, supplementary material. Qualitative descriptions of these explorations are retained in the SAFE text.

Sensitivity Runs indicated by SSC/CIE comments

Truncate survey data before 1993

The SSB time series are basically identical during the period of shared data.

The SSB time series are basically identical during the period of shared data, but the deviations take longer to be informed in the truncated model.

Begin recruitment deviations in 1983

The SSB time series are much higher when the deviations don’t start until later, and end at a slightly lower value.

The recruitment series is not very distinct, though the later-start model estimates recruits as systematically lower for the last ~15 years.

The later-start model fits the first and third survey year better, and reflects a less positive trend (more neutral to flat) than the base model.

Analytical or estimated survey catchability (q)

Both models where q was allowed to estimate/calculate ended up with values around 1.65.

The SSB time series is much smaller when q is allowed to be estimated or calculated analytically. Either approach resulted in very similar time series.

Though the time series are distinct, the status of both models is roughly similar. Note there is some confusion about the horizontal lines here; the SSB series is not actually above B0, rather the Bratios are auto-calculated with respect to B35 (grey dashed line).

Overall the dev trends are similar between models, but the q estimate models are more conservative (smaller devs) in recent years.

These models do a slightly better job fitting the survey data, specifically the decline in the last two years (they cross the CI, at the expense of underfitting the previous ~3 years). My suspicion is that there are some changes in catchability and/or selectivity which aren’t represented in out model, because the fitted trend is quite flat. Alternatively, it’s possible that the population truly is quite stable and our representation of observation error is simply too narrow.

Additional Sensitivity runs

Unweighted Model

This model has no data weights on any source, so the input sample sizes for the compositional data are taken as-is.

Without any data weighting, the SSB trend is orders of magnitude lower than the base model. This suggests that up-weighting the length composition data is the main driver of

Without any data weighting, the SSB trend is orders of magnitude lower than the base model, with a much lower estimate for R0. This suggests that the length composition data are the main drivers of the model scale.

Recruitment deviations vary slightly among approaches; the unweighted model favors more higher recruitment years in the early period, but generally is up and down during the same years as the base.

The unweighted model cannot even enter the ballpark of the survey data, likely because q is fixed to 1.

Francis weights instead of McCallister-Ianelli

The Francis weights are much lower overall than those suggested by M-I.

     Name Type   New_MI New_Francis
1 Fishery  len 1.400244    0.188829
2  Survey  len 1.132556    0.571484
3  Survey  age 0.312384    0.225432

The choice of weighting method does not greatly change the SSB time series, though the Francis data suggest a flatter trend.

Overall the dev trends are similar between models, but the Francis weighted estimate models are more conservative (smaller devs) in recent years.

The model with the Francis weights do a slightly better job fitting the survey data.

The length comp fits are not strikingly different between weighting approaches.

Previous ageing error matrix

Outputs from this approach can be found at the bottom of this page.

Estimate steepness

H was allowed to be estimated and landed right at 0.99, so the resultant outputs are indistinguishable from the original model (with it fixed). It’s hard to say at this point whether we have or do not have evidence for density dependence, particularly because this species is so lightly exploited. For now I am OK with leaving the steepness as-is.

[1] "SR_BH_steep" "SR_LN(R0)"

Steepness was estimated at 0.99, with basically no change in the estimate of R0.

No change in SSB timeseries with h estimated.

No change in dev trends with h estimated.

No change in survey fits with h estimated.